PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture
Por um escritor misterioso
Last updated 10 novembro 2024
A fully automatic methodology to handle the task of segmentation of gliomas in pre-operative MRI scans is developed using a U-Net-based deep learning model that reached high-performance accuracy on the BraTS 2018 training, validation, as well as testing dataset. Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, making it an important tool for effective diagnosis which is requisite to replace the existing manual detection system where patients rely on the skills and expertise of a human. In order to solve this problem, a brain tumor segmentation & detection system is proposed where experiments are tested on the collected BraTS 2018 dataset. This dataset contains four different MRI modalities for each patient as T1, T2, T1Gd, and FLAIR, and as an outcome, a segmented image and ground truth of tumor segmentation, i.e., class label, is provided. A fully automatic methodology to handle the task of segmentation of gliomas in pre-operative MRI scans is developed using a U-Net-based deep learning model. The first step is to transform input image data, which is further processed through various techniques—subset division, narrow object region, category brain slicing, watershed algorithm, and feature scaling was done. All these steps are implied before entering data into the U-Net Deep learning model. The U-Net Deep learning model is used to perform pixel label segmentation on the segment tumor region. The algorithm reached high-performance accuracy on the BraTS 2018 training, validation, as well as testing dataset. The proposed model achieved a dice coefficient of 0.9815, 0.9844, 0.9804, and 0.9954 on the testing dataset for sets HGG-1, HGG-2, HGG-3, and LGG-1, respectively.
Brain Tumor classification and detection from MRI images using CNN based on ResU-Net Architecture, by Sanyukta Suman
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Convolutional neural networks for brain tumour segmentation, Insights into Imaging
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Residual Edge Attention in U-Net for Brain Tumour Segmentation International Journal on Recent and Innovation Trends in Computing and Communication
Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images
Guide to Image Segmentation in Computer Vision: Best Practices
Computers, Free Full-Text
System Architecture Brain Tumor Segmentation using CNN is done mainly
Magnetic resonance image-based brain tumour segmentation methods: A systematic review - Jayendra M Bhalodiya, Sarah N Lim Choi Keung, Theodoros N Arvanitis, 2022
International Journal of Imaging Systems and Technology, IMA
Frontiers MM-UNet: A multimodality brain tumor segmentation network in MRI images
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